Identification of somatic mutations in diverse tumor types has grown exponentially with the development of next-generation sequencing technologies. However, there is a pressing need to validate putative cancer driver genes and separate them from coincidental ?passenger? mutations. Further, it is becoming clear that many cancers are highly heterogeneous in terms of the polyclonality of somatic genotypes?often expressing multiple driver mutations simultaneously or in different subpopulations. Moreover, standard of care treatment often induces selective pressures resulting in significant alterations in recurrent populations. We are only in the beginning stages of validating driver genes in many tumor types and are even further behind in studying mechanisms of evolution and recurrence in these systems. The central theme of this grant application is to generate a toolset marrying patient-derived, ?personalized? somatic mutation signatures with genome editing of synthetic target arrays for lineage tracing (GESTALT) for the elucidation of the transcriptomic mechanisms leading to tumor diversity. Specifically, we will generate a pipeline for isolating single-cell transcriptomes and GESTALT barcodes to classify and lineage map large numbers of tumor populations over time, including after the selective pressures of standard of care treatment. Over the past several years, we have pioneered an electroporation-based somatic mutation method for rapid, non-invasive, somatic transgenesis for high throughput validation of tumor driver genes using mosaic analysis with dual recombinase-mediated cassette exchange (MADR). We will employ novel in vivo MADR models of glioma as a test case for the utilization of this system for later use with diverse tumor types. The overall objective of the proposal is to perform advanced development of this combined MADR-GESTALT approach to allow for generalized use in diverse tumor contexts and, therefore, demonstrate the potential of this technology to transform cancer research. We propose to carry out this work in three parts. The focus of Specific Aim 1 is to optimize the combined MADR-GESTALT system for generating tumor cell classification by transcriptome and lineage maps. The main goal of Specific Aim 2 is to rigorously validate MADR-GESTALT inducible elements in the context of clinical standard of care treatment. Finally, to prepare for widespread dissemination of these tools, in Specific Aim 3 we will generate and validate knock-in mice with GESTALT elements for tissues not amenable to electroporation. Successful completion of these experiments will create the foundation for a long-lived, cornerstone toolset for understanding both basic and pathologic mechanisms of the disease as well as providing definitive, genetic insights into the cellular and transcriptomic mechanisms of progression and recurrence in a diverse array of cancers.